Simultaneous Identification of Bridge Structural Damage and Moving Loads Using the Explicit Form of Newmark-β Method: Numerical and Experimental Studies

Bridge infrastructures are always subjected to degradation because of aging, their environment, and excess loading. Now it has become a worldwide concern that a large proportion of bridge infrastructures require significant maintenance. This compels the engineering community to develop a robust method for condition assessment of the bridge structures. Here, the simultaneous identification of moving loads and structural damage based on the explicit form of the Newmark-β method is proposed. Although there is an extensive attempt to identify moving loads with known structural parameters, or vice versa, their simultaneous identification considering the road roughness has not been studied enough. Furthermore, most of the existing time domain methods are developed for structures under non-moving loads and are commonly formulated by state-space method, thus suffering from the errors of discretization and sampling ratio. This research is believed to be among the few studies on condition assessment of bridge structures under moving vehicles considering factors such as sensor placement, sampling frequency, damage type, measurement noise, vehicle speed, and road surface roughness with numerical and experimental verifications. Results indicate that the method is able to detect damage with at least three sensors, and is not sensitive to sensors location, vehicle speed and road roughness level. Current limitations of the study as well as prospective research developments are discussed in the conclusion.

Allocation Optimization of Multi-Axis Suspension Dynamic Parameter for Tracked Vehicle

The dynamic parameter allocation of the suspension system has an important influence on the comprehensive driving performance of the tracked vehicle. Usually, the allocation of suspension parameters is based on a single performance index, which has the disadvantage of not being able to achieve multi-performance optimization. Therefore, a novel optimization method using multi-performance index-oriented is presented. Firstly, considering the vertical vibration excitation caused by road roughness, the input (excitation) model of road roughness is embedded to establish the parametric dynamic model of the tracked vehicle. Then, the evaluation index and its quantitative algorithm, which reflect the multi-aspect performance of the suspension system, are proposed. Moreover, the parameter allocation objective function based on multi-index information fusion is designed. Finally, two allocation optimization methods are presented to solve the parameter allocation, i.e., equal weight allocation and expert knowledge-based weight allocation. By comparing the results obtained by the two methods, it is found that the performance of the suspension system can be improved effectively by optimizing the parameters of suspension stiffness and damping. Furthermore, the optimization of weight allocation based on expert knowledge is more effective. These provide a better knowledge reference for suspension system design.

Research on Road Roughness Based on NARX Neural Network

In order to solve the problem of road roughness identification, a study on the nonlinear autoregressive with exogenous inputs (NARX) neural network identification method was carried out in the paper. Firstly, a 7-DOF plane model of vehicle vibration system was established to obtain the vertical acceleration and elevation acceleration of the body, which were set as ideal input samples for the neural network. Then, based on the plane model, with common speed, the road roughness was solved as the ideal output sample of the NARX neural network, and the road roughness of B-level and C-level was identified. The results show that the proposed method has ideal identification accuracy and strong antinoise ability. The relative error of C-level road roughness is larger than that of B-level road roughness. The identified road roughness can provide a theoretical basis for analyzing the dynamic response of expressway roads.

METROLOGICAL SUPPORT OF GEODETIC WORKS IN THE ROAD ROUGHNESS MEASURING

Most of the transport and operational indicators that directly affect the road roughness depend on the roughness of coverage. Therefore, the control and timely monitoring of the road roughness is an extremely important issue that needs the attention of road maintenance services. At monitoring of the road roughness it is most expedient to use a technique of leveling of a covering. The method of leveling the coating provides more detailed information about the coating and allows you to determine the smallest deformations on the road coating, which may be at the first stage of their development, especially at that stage of their development, and show roughness and various parameters. One of the main tasks of measurements in the process of performing geodetic works is not only to obtain the measurement result, but also to assess its reliability. The required quality of instrumental measurement can not be achieved without adhering to the principles of unity and the required accuracy of measurements, so much attention should be paid to the metrological support of geodetic works. The purpose of this article is to analyze the metrological support of geodetic works in determining the pavement roughness and substantiation of the required accuracy of measuring the non-rigid pavement roughness. On the basis of dependences for determining the coefficient of dynamic load on pavement and the correlation between the pavement roughness and the coefficient of dynamic load and on the basis of experimental data, the necessary accuracy of measuring the non-rigid pavement roughness is substantiated. Based on the analysis, it was found that the accuracy of determining the height of the irregularities should not exceed 0.5 mm, for which it is necessary to use optical or electron-optical levels.

Evaluating the Dynamic Response of the Bridge-Vehicle System considering Random Road Roughness Based on the Moment Method

The bridge-vehicle interaction (BVI) system vibration is caused by the vehicles passing through the bridge. The road roughness has a great impact on the system vibration. In this regard, poor road roughness is known to affect the comfort of the vehicle crossing the bridge and aggravate the fatigue damage of the bridge. Road roughness is usually regarded as a random process in numerical calculation. To fully consider the influence of road roughness randomness on the response of the BVI system, a random BVI model was established. Thereafter, the random process of road roughness was expressed by Karhunen–Loeve expansion (KLE), after which the moment method was used to calculate the maximum probability value of the BVI system response. The proposed method has higher accuracy and efficiency than the Monte Carlo simulation (MCS) calculation method. Subsequently, the influences of vehicle speed, roughness grade, and bridge span on the impact factor (IMF) were analyzed. The results show that the road roughness grade has a greater impact on the bridge IMF than the bridge span and vehicle speed.

Modal analysis and dynamic shimmy behavior of vehicle–road system

To analyze the influence of the road roughness excitation on vehicle shimmy, a 12 degrees-of-freedom dynamic model of vehicle–road system is developed. The Hopf bifurcation theory is used to study the system stability. On this basis, the natural frequency and modal properties of the vehicle system are elaborated. It can be found that the roll mode plays a crucial role in the vehicle stability. Then, the dynamic shimmy behavior exposed to the sinusoidal and random road roughness excitations is investigated with the help of the modal analysis and the largest Lyapunov exponent. Furthermore, the numerical results are verified through the measurement results, and the influence of the front wheel track on vehicle shimmy is also examined. The results show that the decrease of the front wheel track is an effective way to attenuate vehicle shimmy for different road roughness excitations.